Yuko Sakurai

Robust combinatorial auction protocol against false-name bids.

This paper presents a method for designing bundles in a combinatorial auction protocol that is robust against false-name bids. Internet auctions have become an integral part of Electronic Commerce and a promising field for applying AI technologies. However, the possibility of a new type of cheating called a false-name bid, i.e., a bid submitted under a fictitious name, has been pointed out. n nA protocol called Leveled Division Set (LDS) protocol that is robust against false-name bids has been developed. However, this protocol requires the auctioneer to define a leveled division set. A leveled division set is a series of division sets, where a division set is a set of divisions and a division is a combination of bundles of goods. We need to solve a very complicated optimization problem to construct a leveled division set in order to obtain a good social surplus. n nWe have developed a heuristic method for overcoming this problem. In this method, we first find a good division with a winner determination algorithm, and then construct a leveled division set by using this division as a seed. Through a simulation, we showthat our method can obtain a social surplus that is very close to optimal.

Determining bidding strategies in sequential auctions: quasi-linear utility and budget constraints

In this paper, we develop a new method for finding an optimal bidding strategy in sequential auctions, using a dynamic programming technique. The existing method assumes the utility of a user is represented in an additive form. Thus, the remaining endowment of money must be explicitly represented in each state. On the other hand, our method assumes the utility of a user can be represented in a quasi-linear form, and representing the payment as a state-transition cost. Accordingly, we can obtain more than an

Coalition structure generation in cooperative games with compact representations

m

Core stability in hedonic games among friends and enemies: Impact of Neutrals

-fold speed-up in the computation time, where

Aggregating Crowd Opinions Using Shapley Value Regression

m

Improved WPM encoding for coalition structure generation under MC-nets

is the initial endowment of money. Furthermore, we have developed a method for obtaining a semi-optimal bidding strategy under budget constraints.

Crowdsourcing Mechanism Design

This paper presents a new way of formalizing the coalition structure generation problem (CSG) so that we can apply constraint optimization techniques to it. Forming effective coalitions is a major research challenge in AI and multi-agent systems. CSG involves partitioning a set of agents into coalitions to maximize social surplus. Traditionally, the input of the CSG problem is a black-box function called a characteristic function, which takes a coalition as input and returns the value of the coalition. As a result, applying constraint optimization techniques to this problem has been infeasible. However, characteristic functions that appear in practice often can be represented concisely by a set of rules, rather than treating the function as a black box. Then we can solve the CSG problem more efficiently by directly applying constraint optimization techniques to this compact representation. We present new formalizations of the CSG problem by utilizing recently developed compact representation schemes for characteristic functions. We first characterize the complexity of CSG under these representation schemes. In this context, the complexity is driven more by the number of rules than by the number of agents. As an initial step toward developing efficient constraint optimization algorithms for solving the CSG problem, we also develop mixed integer programming formulations and show that an off-the-shelf optimization package can perform reasonably well.

Coalition Structure Generation for Partition Function Games Utilizing a Concise Graphical Representation

We investigate hedonic games under enemies aversion and friends appreciation, where every agent considers other agents as either a friend or an enemy. We extend these simple preferences by allowing each agent to also consider other agents to be neutral. Neutrals have no impact on her preference, as in a graphical hedonic game. Surprisingly, we discover that neutral agents do not simplify matters, but cause complexity. We prove that the core can be empty under enemies aversion and the strict core can be empty under friends appreciation. Furthermore, we show that under both preferences, deciding whether the strict core is nonempty, is NPNP-complete. This complexity extends to the core under enemies aversion. We also show that under friends appreciation, we can always find a core stable coalition structure in polynomial time.

Robust multi-unit auction protocol against false-name bids

Crowdsourcing is becoming increasingly popular in various tasks. Aggregating answers from workers in crowdsouring has been a widely used technique for providing many applications and services. To aggregate these answers, fair evaluation of workers is important to motivate them to give high quality answers. However, it is difficult to fairly evaluate workers if their answers show a high degree of correlation. In this paper, we propose to use the Shapley value regression as a means to address this problem. The regression technique is based on ideas developed from cooperative game theory to evaluate the relative importance of explanatory variables in reducing the error. We also exploit sparseness of worker collaboration graph to effectively calculate the Shapley value, since it requires an exponential computation time to calculate the Shapley value.

Bundle design in robust combinatorial auction protocol against false-name bids

The Coalition Structure Generation (CSG) problem plays an important role in the domain of coalition games. Its goal is to create coalitions of agents so that the global welfare is maximized. To date, Weighted Partial MaxSAT (WPM) encoding has shown high efficiency in solving the CSG problem, which encodes a set of constraints into Boolean propositional logic and employs an off-the-shelf WPM solver to find out the optimal solution. However, in existing WPM encodings, a number of redundant encodings are asserted. This results in additional calculations and correspondingly incurs performance penalty. Against this background, this paper presents an Improved Rule Relation-based WPM (I-RWPM) encoding for the CSG problem, which is expressed by a set of weighted rules in a concise representation scheme called Marginal Contribution net (MC-net). In order to effectively reduce the constraints imposed on encodings, we first identify a subset of rules in an MC-net, referred as a set of freelance rules. We prove that solving the problem made up of all freelance rules can be achieved with a straightforward means without any extra encodings. Thus the set of rules requiring to be encoded is downsized. Next, we improve the encoding of transitive relations among rules. To be specific, compared with the existing rule relation-based encoding that generates transitive relations universally among all rules, I-RWPM only considers the transitivity among rules with particular relationship. In this way, the number of constraints to be encoded can be further decreased. Experiments suggest that I-RWPM significantly outperforms other WPM encodings for solving the same set of problem instances.